Improving the Performance of Mutation-based Fault Localization via Mutant Bias Practical Experience Report
Bin Du, Yuxiaoyang Cai, Haifeng Wang, Yong Liu, Xiang Chen · 2022
Mutation-Based Fault Localization (MBFL) is one of the most widely studied techniques. MBFL adopts mutation analysis to generate mutants for revealing potential faults in the program. Previous studies proposed approaches to optimize MBFL in terms of efficiency and accuracy. However, these approaches ignored the difference of mutants on correct entities (such as statements) and faulty entities, which we refer this kind of difference as mutant bias. In this study, we identify and analyze the impact of mutant bias on MBFL. We find that the mutant bias may introduce effects to statement suspiciousness and negatively influence the fault localization accuracy of MBFL. To mitigate the mutant bias, we propose Delta4Ms, a model that captures the mutant bias from the mutants of the same statements. Then the real suspiciousness is obtained by removing the bias from the practical suspiciousness. To evaluate the performance of our proposed method, we conduct experimental studies on 320 real-world programs from Codeflaws. The experimental results show that Delta4Ms improves the fault localization accuracy of MBFL. Besides, Delta4Ms outperforms the state-of-the-art SBFL and three MBFL techniques significantly. Moreover, Delta4Ms ranks 94 and 161 of the target faults within the top-5 suspicious statements in single-fault and multiple-fault programs, respectively.